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Comparative Assessment of Empirical Methods for Bored Pile Capacity Prediction Against Static Load Test Data in Indonesia Rahmat kurniawan; Chindy Akila; Rifky Fauzi; Ayu Sinta Aprilia; Yunita Asni; Ahmad Auliadi Y
Rekayasa Sipil Vol. 20 No. 2 (2026): Rekayasa Sipil Vol. 20 No. 2
Publisher : Department of Civil Engineering, Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.rekayasasipil.2026.020.02.11

Abstract

Accurate estimation of the bearing capacity of bored-pile foundations is essential to ensure both the safety and cost-effectiveness of foundation design. However, empirical design formulas are derived from simplified assumptions and may not fully represent actual soil–pile behavior in the field, leading to overestimation or underestimation of capacity if their performance is not carefully evaluated. This study compares the predictive performance of three SPT-based empirical methods—Meyerhof (1976), Reese & Wright (1977), and O’Neill & Reese (1999)—against Static Load Test (SLT) results interpreted using the Davisson, Chin, and Mazurkiewicz methods. A database of 10 bored-pile projects from various regions across Indonesia was analyzed. Given the limited sample size (n = 10), all findings are presented as preliminary evidence rather than definitive conclusions, and statistical estimates carry substantial uncertainty that should inform interpretation. Statistical evaluation employed correlation analysis (r, R²), bias factor (?), coefficient of variation (COV), and mean absolute percentage error (MAPE). Results indicate that the O’Neill & Reese method demonstrates the strongest correlation with Davisson-interpreted SLT results (R² = 0.853), while the Meyerhof method yields a mean bias factor closest to unity (? = 1.00). A performance ranking matrix is informed by concepts commonly adopted in LRFD calibration studies. These results indicate that differences in predictive performance reflect how each empirical formulation represents field behavior and suggest differentiated use of methods depending on the design stage. However, recommendations require validation against larger databases before widespread adoption.